Tracking SIS performance with historical data can improve analytics

Plant operations, maintenance can benefit from a better understanding of system performance.

By Jeremy M Lucas, Mangan Software Solutions April 20, 2016

This illustrates how combining aggregate data and test object data can deliver better analytics. Courtesy: Mangan Software SolutionsBy leveraging the power of real-world plant historical device event data and incorporating that data into a safety instrumented system (SIS), safety lifecycle management (SLM) software, plant operations, and maintenance teams can gain powerful insights into the performance of their SIS while responding quickly to maintenance issues.

A number of SLM software platforms on the market are capable of providing its users with high value data regarding the performance of plant protective systems through an operations and maintenance functionality. These typically report on key performance indicators such as:

  • Testing records and management of testing due dates
  • Evaluation of device performance over time (prior in-use)
  • Assessment of the overall performance of protective functions including evaluation of demand rates and failure rates
  • Bypasses of protective functions
  • Overall plant and unit performance indicators.

Realization of these benefits requires that performance data for protective functions and devices used to implement those functions should be entered into an SLM software promptly and routinely. This is often a significant obstacle as diverse personnel are involved in capturing and entering the event data for demands, failures, bypasses, etc. and acquiring a complete picture can be challenging.

One method of addressing this problem is to maximize the amount of data that can be collected automatically. This involves making the event data available to process historians where it can be readily analyzed by the SLM software.

At the SIS level, detection of events of interest should be programmed into the SIS and made available to the basic process controls systems (BPCS) and historians. This includes programming to identify common events such as:

  • Demands upon the SIS and other auxiliary functions generated by the SIS logic
  • Capture of the source of demands, whether from a process demand (e.g. a low flow) or a manual demand such as a shutdown command
  • Feedback and monitoring of the final device command vs. state and capturing events where a final element may not have responded as expected.
  • Monitoring of inputs and generating status data for events such as device failures, deviations in voting inputs, etc.
  • Identification of when inputs or outputs associated with protective functions have been bypassed or overridden.

The net result is a vastly improved management of protective systems for process safety, faster identification, and resolution of performance problems. These results also mean improvements in protective safety function reliability, reduction in costs associated with testing, maintenance, and false trips.

SLM software can leverage real-world plant data to augment manual data entry processes, with automated event identification and analytics to assist in safety performance identification across the entire plant. Incorporating this level of data and near real-time data streams is made simpler using an SLM software platform with a flexible open architecture and process historian integration tools. This will lead to a safer, more efficient plant and a greater understanding of the overall performance of the plant’s safety systems.